
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  int	   =      (DDPMScheduler,)


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   {
       """num_train_timesteps""": 10_00,
       """beta_start""": 0.0_0_0_1,
       """beta_end""": 0.0_2,
       """beta_schedule""": """linear""",
       """variance_type""": """fixed_small""",
       """clip_sample""": True,
   }

   config.update(**SCREAMING_SNAKE_CASE_)
   return config


 def   lowercase__  (       self):


   '''simple docstring'''




   for timesteps in [1, 5, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]       ,	[0.0_0_2, 0.0_2, 0.2, 2]):
     self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,	beta_end=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for variance in ["fixed_small", "fixed_large", "other"]:
     self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_)
   for threshold in [0.5, 1.0, 2.0]:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(
           thresholding=SCREAMING_SNAKE_CASE_       ,	prediction_type=SCREAMING_SNAKE_CASE_       ,	sample_max_value=SCREAMING_SNAKE_CASE_       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   for prediction_type in ["epsilon", "sample", "v_prediction"]:
     self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for t in [0, 5_00, 9_99]:
     self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.scheduler_classes[0]
   lowercase__	: Union[str, Any]     		=   self.get_scheduler_config()
   lowercase__	: List[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0_9_7_9)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.0_2)) < 1E-5


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.scheduler_classes[0]
   lowercase__	: str     		=   self.get_scheduler_config()
   lowercase__	: Tuple     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   len(SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   self.dummy_model()
   lowercase__	: List[Any]     		=   self.dummy_sample_deter
   lowercase__	: str     		=   torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__	: List[str]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_).prev_sample

     # if t > 0:
     #     noise = self.dummy_sample_deter
     #     variance = scheduler.get_variance(t) ** (0.5) * noise
     #
     # sample = pred_prev_sample + variance
     lowercase__	: str     		=   pred_prev_sample

   lowercase__	: Optional[int]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: Optional[Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 2_5_8.9_6_0_6) < 1E-2
   assert abs(result_mean.item() - 0.3_3_7_2) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config(prediction_type="""v_prediction""")
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   len(SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   self.dummy_model()
   lowercase__	: Union[str, Any]     		=   self.dummy_sample_deter
   lowercase__	: int     		=   torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__	: List[Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__	: int     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_).prev_sample

     # if t > 0:
     #     noise = self.dummy_sample_deter
     #     variance = scheduler.get_variance(t) ** (0.5) * noise
     #
     # sample = pred_prev_sample + variance
     lowercase__	: Tuple     		=   pred_prev_sample

   lowercase__	: Union[str, Any]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: int     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 2_0_2.0_2_9_6) < 1E-2
   assert abs(result_mean.item() - 0.2_6_3_1) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: str     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   [1_00, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   scheduler.timesteps

   for i, timestep in enumerate(SCREAMING_SNAKE_CASE_):
     if i == len(SCREAMING_SNAKE_CASE_) - 1:
       lowercase__	: Optional[int]     		=   -1
     else:
       lowercase__	: Tuple     		=   timesteps[i + 1]

     lowercase__	: Any     		=   scheduler.previous_timestep(SCREAMING_SNAKE_CASE_)
     lowercase__	: int     		=   prev_t.item()

     self.assertEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: List[Any]     		=   self.get_scheduler_config()
   lowercase__	: int     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   [1_00, 87, 50, 51, 0]

   with self.assertRaises(SCREAMING_SNAKE_CASE_       ,	msg="""`custom_timesteps` must be in descending order."""):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.scheduler_classes[0]
   lowercase__	: List[Any]     		=   self.get_scheduler_config()
   lowercase__	: int     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   [1_00, 87, 50, 1, 0]
   lowercase__	: Union[str, Any]     		=   len(SCREAMING_SNAKE_CASE_)

   with self.assertRaises(SCREAMING_SNAKE_CASE_       ,	msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""):
     scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE_       ,	timesteps=SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   [scheduler.config.num_train_timesteps]

   with self.assertRaises(
       SCREAMING_SNAKE_CASE_       ,	msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""       ,	):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)













'''simple docstring'''






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def   __A    (				):
		lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= 9, 1_4  # noqa: F841
		lowerCAmelCase  :     int       						= [
		    [0, 1, 4],
		    [0, 7, 8],
		    [1, 2, 8],
		    [7, 8, 7],
		    [7, 6, 1],
		    [2, 8, 2],
		    [8, 6, 6],
		    [2, 3, 7],
		    [2, 5, 4],
		    [6, 5, 2],
		    [3, 5, 1_4],
		    [3, 4, 9],
		    [5, 4, 1_0],
		    [1, 7, 1_1],
		]

		lowerCAmelCase  :     Optional[Any]       						= defaultdict(a_						)
		for nodea, nodea, cost in edges:
				adjancency[nodea].append([nodea, cost]						)
				adjancency[nodea].append([nodea, cost]						)

		lowerCAmelCase  :     int       						= mst(a_						)

		lowerCAmelCase  :     Tuple       						= [
		    [7, 6, 1],
		    [2, 8, 2],
		    [6, 5, 2],
		    [0, 1, 4],
		    [2, 5, 4],
		    [2, 3, 7],
		    [0, 7, 8],
		    [3, 4, 9],
		]

		for answer in expected:
				lowerCAmelCase  :     List[str]       						= tuple(answer[:2]						)
				lowerCAmelCase  :     int       						= tuple(edge[::-1]						)
				assert edge in result or reverse in result



